Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics
Autoři:
Bryn Cloud aff001; Britt Tarien aff001; Ada Liu aff001; Thomas Shedd aff001; Xinfan Lin aff001; Mont Hubbard aff001; R. Paul Crawford aff002; Jason K. Moore aff001
Působiště autorů:
Mechanical and Aerospace Engineering, University of California Davis, Davis, California, United States of America
aff001; Hegemony Technologies LLC, Davis, California, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225690
Souhrn
Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics.
Klíčová slova:
Acceleration – Accelerometers – Boats – Cell phones – Kalman filter – Kinematics – Signal filtering – Sports
Zdroje
1. Winter DA. Biomechanics and Motor Control of Human Movement. John Wiley & Sons; 2009.
2. Lee L, Jones M, Ridenour GS, Bennett SJ, Majors AC, Melito BL, et al. Comparison of Accuracy and Precision of GPS-Enabled Mobile Devices. In: 2016 IEEE International Conference on Computer and Information Technology (CIT); 2016. p. 73–82.
3. Zhizhong Ma, Yuansong Qiao, Lee B, Fallon E. Experimental Evaluation of Mobile Phone Sensors. In: 24th IET Irish Signals and Systems Conference (ISSC 2013). Letterkenny, Ireland: Institution of Engineering and Technology; 2013. p. 49–49.
4. Smith TB, Hopkins WG. Measures of Rowing Performance. Sports Medicine. 2012;42(4):343–358. doi: 10.2165/11597230-000000000-00000 22401296
5. Kleshnev V. Biomechanics of Rowing. Crowood Press; 2016.
6. Cejuela Anta R, Turpin P, Antonio J, Cortell-Tormo JM, Mira C, José J. An Analysis of High Performance in Long-Distance Rowing by Means of Global Positioning System Technology. In: Symposium Proceedings 6TM IACSS; 2008. p. 1–4.
7. Simões ND, Gonçalves JL, Caeiro ML, Boavida MJ, Cardoso FD. ZigBee/GPS Tracking System for Rowing Races. In: 2011 IEEE EUROCON—International Conference on Computer as a Tool; 2011. p. 1–4.
8. Ai K, He S. Development of a Speed Measurement System with Globle Positioning System in Rowing and Canoeing. In: ISBS-Conference Proceedings Archive. vol. 1; 2000. p. 1.
9. Zhang K, Deakin R, Grenfell R, Li Y, Zhang J, Cameron WN, et al. GNSS for Sports—Sailing and Rowing Perspectives. Journal of Global Positioning Systems. 2004;3(1&2):280–289.
10. Hedgecock W, Maroti M, Sallai J, Volgyesi P, Ledeczi A. High-Accuracy Differential Tracking of Low-Cost GPS Receivers. In: MobiSys 2013. ACM Press; 2013. p. 221.
11. Kiam JJ, Cardenas JM, Henkel P. Cost-Effective Cooperative RTK Positioning for Rowing Boats. In: Proc. of ION International Technical Meeting (ITM); 2014. p. 541–550.
12. Hill H, Fahrig S. The Impact of Fluctuations in Boat Velocity during the Rowing Cycle on Race Time. Scandinavian Journal of Medicine & Science in Sports. 2009;19(4):585–594. doi: 10.1111/j.1600-0838.2008.00819.x
13. Waegli A, Skaloud J. Optimization of Two GPS/MEMS-IMU Integration Strategies with Application to Sports. GPS Solutions. 2009;13(4):315–326. doi: 10.1007/s10291-009-0124-5
14. Groh BH, Reinfelder SJ, Streicher MN, Taraben A, Eskofier BM. Movement Prediction in Rowing Using a Dynamic Time Warping Based Stroke Detection. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP); 2014. p. 1–6.
15. Janssen I, Sachlikidis A. Validity and Reliability of Intra-Stroke Kayak Velocity and Acceleration Using a GPS-Based Accelerometer. Sports Biomechanics. 2010;9(1):47–56. doi: 10.1080/14763141003690229 20446639
16. Mpimis A, Gikas V. Monitoring and Evaluation of Rowing Performance Using Mobile Mapping Data. Archives of Photogrammetry, Cartography and Remote Sensing. 2011;22:337–349.
17. Ruffaldi E, Peppoloni L, Filippeschi A. Sensor Fusion for Complex Articulated Body Tracking Applied in Rowing. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. 2015;229(2):92–102.
18. Anderson R, Harrison A, Lyons GM. Accelerometry-Based Feedback—Can It Improve Movement Consistency and Performance in Rowing? Sports Biomechanics. 2005;4(2):179–195. doi: 10.1080/14763140508522862 16138656
19. Tessendorf B, Gravenhorst F, Arnrich B, Tröster G. An IMU-Based Sensor Network to Continuously Monitor Rowing Technique on the Water. In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing; 2011. p. 253–258.
20. Schindhelm CK, Gschwandtner F, Banholzer M. Usability of Apple iPhones for Inertial Navigation Systems. In: 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications; 2011. p. 1254–1258.
21. Hermsen H. Using GPS and Accelerometer Data for Rowing Race Tracking [Master]. University of Groningen. Groningen, Netherlands; 2013.
22. NIMA. Department of Defense World Geodetic System 1984, Its Definition and Relationships With Local Geodetic Systems. National Imagery and Mapping Agency; 2000. TR8350.2.
23. Anonymous JP. Peak Signal Detection in Realtime Timeseries Data; 2014. https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-timeseries-data.
24. van den Bogert AJ, Geijtenbeek T, Even-Zohar O, Steenbrink F, Hardin EC. A Real-Time System for Biomechanical Analysis of Human Movement and Muscle Function. Medical & Biological Engineering & Computing. 2013; p. 1–9.
25. Kalman RE. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering. 1960;82(Series D):35–45. doi: 10.1115/1.3662552
26. Grewal M, Andrews AP. Kalman Filtering: Theory and Practice Using MATLAB. 3rd ed. John Wiley & Sons, Inc; 2008.
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- Pomohou nám s detekcí karcinomu plic i včely?
- Duševní onemocnění se u teenagerů přenáší v rámci školní třídy
- Od kostiček k lidské kůži. Jak si postavit 3D biotiskárnu?
- Řekněte mi, co jíte, a já vám řeknu, jaká je úroveň vaší kognice
- Umělá inteligence v mozkovém implantátu pomáhá muži po mrtvici s dvojjazyčnou konverzací
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy